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Tuomas SandholmView profile

Professor

• Artificial Intelligence
• Game Theory
• Algorithms and Complexity
• +12 more

Tuomas Sandholm is the Angel Jordan University Professor of Computer Science at Carnegie Mellon University, where he also holds appointments in the Machine Learning Department, the Ph.D. Program in Algorithms, Combinatorics, and Optimization, and the CMU/University of Pittsburgh Joint Ph.D. Program in Computational Biology. He serves as Co-Director of CMU AI and Director of the Electronic Marketplaces Laboratory. Dr. Sandholm's research spans artificial intelligence, economics, and operations research, with particular focus on algorithms and complexity, game theory, machine learning, and electronic commerce. His work involves developing incentive-compatible market mechanisms and efficient algorithms for executing those mechanisms, as well as designing software agents that act optimally in electronic marketplaces. His research has led to significant commercial applications, including systems fielded by CombineNet, Inc. that handled over $60 billion in spend. Sandholm's recent publications reveal a strong emphasis on solving large-scale games, equilibrium computation, and applying these techniques to real-world problems. His work shows a progression from theoretical foundations to practical implementations, with applications ranging from poker AI (Libratus and Pluribus) to kidney exchange systems. His research group has pioneered techniques in game abstraction, regret minimization, and equilibrium computation that have pushed the boundaries of what's possible in imperfect-information games. Vannevar Bush Faculty Fellowship (2023) AAAI Award for AI for the Benefit of Humanity (2023) IJCAI John McCarthy Award (2021) Robert S. Engelmore Award (2021) Minsky Medal (2019) Science Breakthrough of the Year Runner-Up (2019) As an advisor, Sandholm has mentored numerous PhD students who have gone on to prestigious positions at institutions like MIT, Stanford, and CMU. His lab has secured substantial research funding for projects including solving large-scale games and heart transplantation policy optimization. The Electronic Marketplaces Laboratory he directs has been instrumental in developing algorithms that run the national kidney exchange for UNOS, resulting in approximately 10,000 life-saving transplants. Sandholm is also the founder and CEO of multiple companies including Strategy Robot, Inc., Strategic Machine, Inc., and Optimized Markets, Inc., which apply his research to defense, intelligence, business strategy, and advertising markets.

Srinivasan SeshanView profile

Professor

• Distributed Systems
• Mobile Computing
• Networking
• +1 more

Srinivasan Seshan is Joseph Traub Professor of Computer Science and Department Head of the Computer Science Department at Carnegie Mellon University's School of Computer Science. His research focuses on network protocols, distributed network applications, and next-generation network architectures. His primary research explores next-generation network architectures like the eXpressive Internet Architecture (XIA) for robust internet evolution and video content delivery optimization. A secondary focus examines systems challenges in mobile computing, including power-efficient data collection, scalable infrastructure design, and privacy management for smartphone-based environmental observations. As a prominent researcher in distributed systems, Seshan has made significant contributions to transport protocols, sensor networking, and firewall design. His publications demonstrate consistent focus on network architecture innovation and mobile systems optimization.

Peter SteenkisteView profile

Professor

• Distributed Systems
• Networking
• Security and Privacy
• +2 more

Peter Steenkiste is a Professor of Computer Science and Electrical and Computer Engineering at Carnegie Mellon University, with appointments in the School of Computer Science and the Computer Science Department. His research focuses on distributed systems, networking, and wireless technologies, including future Internet architectures like XIA and applications in 5G networks. Steenkiste holds a Ph.D. in Electrical Engineering from Stanford University. His work integrates theoretical and applied approaches, often involving prototype development and real-world testing. Research interests include: Future Internet Architecture (XIA) Wireless network optimization Dynamic spectrum access Network telemetry His publications consistently address network efficiency and innovation, with recent works exploring low-latency video delivery, 5G virtualization, and programmable network infrastructure. Awards include IEEE and ACM Fellowships, Andrew Heiskell Honorable Mention, and University of Cantabria Research Award. He has advised students like Shawn Chen and secured grants for projects including REACH (Robust Content Delivery) and FLOYD (Vehicular Edge Computing). He leads research groups at CMU's Gates and Hillman Centers and collaborates with industry partners like Telefonica and ETH Zurich.

Riad WahbyView profile

Assistant Professor

• Cryptography
• Zero-Knowledge Proofs
• Secure Computation
• +1 more

Riad Wahby is an Assistant Professor in the Electrical and Computer Engineering department at Carnegie Mellon University's College of Engineering, with affiliations in the Computer Science Department. His research focuses on theoretical computer science with applications in cryptography and security systems. Primary research areas include: Zero-knowledge proof systems and SNARKs Secure multiparty computation Cryptographic protocol design Formal verification methods His publications demonstrate consistent innovation in cryptographic compilers, constraint systems for SNARKs, and practical applications of zero-knowledge proofs. Recent work highlights include advancements in finite-field verification, decentralized auction systems, and side-channel attack analysis. Current advising includes graduate students Quang Dao and Kunming Jiang. Teaching includes courses in computer systems and cryptography (15-330).

Zhihao JiaView profile

Assistant Professor

• Artificial Intelligence
• Systems
• Quantum Computing
• +3 more

Zhihao Jia is an Assistant Professor in the Computer Science Department at Carnegie Mellon University (CMU). He is affiliated with the CMU Catalyst Group and the Parallel Data Lab, focusing on advancing systems for machine learning, quantum computing, and large-scale data analytics. Previously, he was a research scientist at Facebook and earned his PhD from Stanford University (2020), advised by Alex Aiken and Matei Zaharia. His bachelor's degree is from Tsinghua University's Special Pilot CS Class under Andrew Yao. His research emphasizes accelerating deep learning computations on modern hardware and optimizing quantum circuits for intermediate-scale quantum devices. Notable contributions include speculative reasoning techniques, efficient LLM serving systems, and quantum circuit simulators. He teaches advanced courses such as 15418 and 15618 at CMU. Zhihao advises PhD students including Zhuoming Chen, Zikun Li, and Xinhao Cheng. His work bridges systems research with emerging applications, aiming to enhance computational efficiency and scalability. He collaborates on projects like Specexec, Helix, and Atlas, addressing challenges in distributed computing and quantum simulation.

Eric XingView profile

Professor

• Artificial Intelligence
• Machine Learning
• Bioinformatics
• +3 more

Eric Xing is a Professor in the Machine Learning Department at Carnegie Mellon University, with an affiliation to the Computer Science Department. His research focuses on advancing artificial intelligence, particularly in machine learning, bioinformatics, and interdisciplinary applications. He advises three students: Aviv Bick, Lingjing Kong, and Yonghao Zhuang. His recent work spans cutting-edge topics such as graph neural networks, generative AI, biosecurity safeguards, and scalable language model evaluation. Notable contributions include foundational models for RNA prediction, efficient video diffusion systems, and ethical frameworks for AI deployment. He actively bridges AI with fields like genomics, healthcare, and urban planning through collaborative projects. Xing’s research often emphasizes practical applications, such as edge computing-optimized inference systems and open mathematical corpora. His work has implications for both technical advancements and societal challenges, reflecting a commitment to impactful, interdisciplinary AI research.

Ryan O'DonnellView profile

Professor

• Algorithms and Complexity
• Security and Privacy
• Theoretical Computer Science
• +6 more

Ryan O'Donnell is a Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. He specializes in theoretical computer science, with a focus on algorithms, complexity theory, quantum computing, and probability. His research includes Fourier analysis of Boolean functions, constraint satisfaction problems, and quantum information theory. He teaches courses such as 15251 (Fall 2025), 15754 (Spring 2025), and others. His work spans quantum tomography, pseudorandomness, and algorithmic design. Recent publications explore topics like quartic quantum speedups, uniformity testing, and explicit expanders. He advises PhD students William He, Noah Singer, and Jingxun Liang. His research interests bridge foundational theory and practical applications, including healthcare informatics and cryptographic tools. He actively contributes to academic conferences and program committees, reflecting his leadership in the field.

• Artificial Intelligence
• Optimization
• Game Theory
• +7 more

Tuomas Sandholm is the Angel Jordan University Professor of Computer Science at Carnegie Mellon University (CMU), holding appointments in the Computer Science Department, Machine Learning Department, Algorithms, Combinatorics, and Optimization (ACO) Ph.D. Program, and the CMU/University of Pittsburgh Joint Ph.D. Program in Computational Biology. He co-directs CMU AI and leads the Electronic Marketplaces Laboratory. His research focuses on artificial intelligence, economics, and operations research, with notable contributions to game theory, optimization, and kidney exchange systems. Sandholm has pioneered algorithms for electronic marketplaces, including the first superhuman AI in poker (Libratus and Pluribus) and kidney exchange optimization. He has founded multiple companies, including CombineNet, Strategic Machine, and Strategy Robot, and has over 500 peer-reviewed publications and 29 patents. His awards include the Vannevar Bush Faculty Fellowship, AAAI Award for AI for Humanity, and honorary doctorate from the University of Zurich. Education: Ph.D. and M.S. in Computer Science Dipl. Eng. (M.S. with B.S. included) in Industrial Engineering and Management Science Research Interests: Artificial Intelligence, Optimization, Game Theory, Kidney Exchange, Electronic Commerce, Multiagent Systems, Market Design, and Healthcare Applications. His work bridges theoretical advancements with real-world applications, particularly in strategic reasoning, organ transplantation, and defense/intelligence systems. Recent Articles: Focus on solving large-scale games, kidney exchange optimization, and AI-driven decision-making. Key areas include imperfect-information game solving (e.g., poker AIs), algorithmic fairness in healthcare markets, and combinatorial auction design. Awards & Honors: Vannevar Bush Faculty Fellowship (2023) AAAI Award for AI for the Benefit of Humanity (2023) Fellowships: ACM, AAAI, INFORMS, AAAS Edelman Laureate (2005) Over 100+ awards, including honorary doctorates and industry recognitions Advising & Grants: Advised PhD students now in top positions at MIT, Stanford, and CMU. Secured grants for AI game-solving and healthcare policy optimization. Current openings for PhD students in game-solving and kidney exchange policy. Labs & Teams: Electronic Marketplaces Laboratory (EML) and CMU AI Initiative. Collaborates with industry partners (e.g., Baidu, Google) and government agencies on strategic AI applications.

• Machine Learning
• Statistics
• Neuroscience
• +4 more

Josue Orellana is a Research Professor in the Department of Statistics & Data Science at Carnegie Mellon University (CMU) and serves as Managing Editor of the Computational Neuroscience Navigator initiative. He holds a PhD in Machine Learning and Neural Computation from CMU (2019) and a B.S. in Electrical Engineering from Washington State University. Previously, he worked as a Research Scientist at the National University of Singapore and Johns Hopkins University. His research integrates Statistics , Machine Learning , and Computational Neuroscience to model brain network interactions during cognitive tasks. He also investigates statistics education methodologies through the CMU Teach Stat research group. His work emphasizes graphical network analysis, phase coupling in neural circuits, and educational cognitive science. Publications span neuroscience, machine learning, and statistics education, with recent articles exploring neural oscillations, educational assessment tools, and multivariate phase coupling. His scholarly output demonstrates consistent focus on statistical methodology in neural systems and innovations in STEM pedagogy . Awards & Grants: CMU Simon Seed Grant (2020) GuSH cross-walk statistics education grant (2019) CMU Presidential Fellowship (2016-2018) President’s Honor Roll, WSU (2008-2012) He leads the Computational Neuroscience Navigator project at CMU’s Open Learning Initiative, developing concept-driven video resources. No advising relationships or lab affiliations are detailed beyond his educational research group.

• Uncertainty Quantification
• Causal Inference
• Conformal Prediction
• +3 more

Ying Jin is an Assistant Professor in the Department of Statistics and Data Science at the Wharton School, University of Pennsylvania. She received her PhD in Statistics from Stanford University in 2024, advised by Emmanuel Candès and Dominik Rothenhäusler, and holds a B.S. in Mathematics and B.A. in Economics from Tsinghua University. Current faculty at UPenn Wharton Former postdoctoral fellow at Harvard Data Science Initiative Her research focuses on Uncertainty Quantification and Generalizability in AI models, particularly through conformal prediction , causal inference , and multiple testing . Recent work explores distribution shifts in large-scale replication studies and methods for trustworthy AI in drug discovery and medical applications. Key article trends show expertise in: Conformal prediction methods Causal inference under distribution shifts LLM-driven scientific discovery Replicability analysis AI uncertainty quantification High-stakes AI validation Scientific awards include: 2025 IMS Lawrence D. Brown PhD Student Award 2024 Jack Youden Prize for best expository paper in Technometrics She organizes the Online Causal Inference Seminar and contributes to open science through the awesome-replicability-data GitHub repository containing curated replication datasets.

Samuli ReijulaView profile

Associate Professor

• Philosophy of Science
• Cognitive Science
• Social Epistemology
• +5 more

Samuli Reijula is a permanent university lecturer (equivalent to US Associate Professor) in Theoretical Philosophy at the University of Helsinki, with additional affiliation as a docent (habilitation) in Practical Philosophy. He is also associated with the TINT Centre for Philosophy of Social Science and leads his personal research section 'Reijulab.' His research focuses on scientific problem-solving as a distributed cognitive system, examining science at multiple levels from individual scientists to entire communities. Key interests include social epistemology, AI ethics, epistemic humility, diversity in science, and institutional epistemology. Reijula's work critically examines the relationship between scientific communities and society, emphasizing the importance of academic autonomy and trust. His recent publications analyze AI's philosophical implications, diversity policies in research, academic recruitment policies, and the enduring value of the Bildung university model. Reijula has contributed to debates on scientific uncertainty, open science, and the philosophical foundations of collective problem-solving in scientific communities. Reijula currently holds an Academy of Finland research project (2020-2025) titled 'Modeling the republic of science: Collaborative problem solving and collective rationality in scientific inquiry.' He has collaborated with prominent scholars including Jaakko Kuorikoski, Kristina Rolin, and Petri Ylikoski on topics ranging from institutional epistemology to agent-based modeling in social epistemology. As an active public intellectual, Reijula contributes to Finnish media outlets including Helsingin Sanomat, Suomen Kuvalehti, and Tietessä Tapahtuu, where he analyzes science policy issues and philosophical dimensions of contemporary scientific challenges.

• AI Ethics
• Philosophy of Computing
• AI Safety
• +3 more

Seth Lazar is a Professor of Philosophy at the Australian National University (ANU) and holds multiple distinguished roles: Distinguished Research Fellow at the University of Oxford Institute for Ethics in AI, Fellow of the Carnegie Endowment for International Peace, and Senior AI Advisor to the Knight First Amendment Institute at Columbia University. He founded the Machine Intelligence and Normative Theory (MINT) Lab, focusing on philosophy of computing and AI safety. His research bridges ethics, technology, and governance, addressing societal impacts of AI, algorithmic governance, and communicative justice. Recent work explores AI's role in democracy, attention distribution, and economic systems. Lazar regularly contributes to public discourse through media interviews and op-eds, such as his Guardian article questioning AI's role in public policymaking. Lazar leads interdisciplinary projects via the MINT Lab and collaborates with institutions like Yale Law School on normative philosophy of computing. He delivers high-profile lectures (e.g., Scholl Lecture at Purdue) and organizes workshops on AI ethics and democratic freedoms. His work emphasizes proactive evaluation of emerging AI technologies to anticipate risks and align them with ethical goals.

Benjamin MoseleyView profile

Associate Professor

• Operations Research
• Theoretical Computer Science
• Machine Learning
• +5 more

Benjamin Moseley is the Carnegie Bosch Associate Professor of Operations Research at the Tepper School of Business, Carnegie Mellon University (CMU), with a courtesy appointment in the School of Computer Science's Machine Learning Department. He is also a consulting professor at the start-up Relational AI. Education: PhD in Computer Science from the University of Illinois, advised by Chandra Chekuri. Prior roles include Assistant Professor at Washington University in St. Louis (2014–2017), Research Assistant Professor at Toyota Technological Institute at Chicago (2012–2014), and visiting positions at Sandia National Laboratories and Yahoo Labs. Notable affiliations include the NSF-funded Algorithms, Combinatorics, and Optimization (ACO) PhD program. Research focuses on algorithm design, analysis, and evaluation, with emphasis on operations research, theoretical computer science, and machine learning. Current projects include algorithmic foundations of machine learning, distributed algorithm design, and scheduling/logistics optimization. His work bridges theoretical rigor and practical applications, addressing challenges in big data analysis, streaming algorithms, and approximation techniques. Recognition includes multiple best paper awards (IPDPS 2015, SPAA 2013, SODA 2010), top-tier NeurIPS presentations, and prestigious grants from NSF, ONR, Google, Bosch, and others. His research has been supported by a NSF CAREER Award, Google Faculty Research Awards, and the Carnegie-Bosch Chair. Teaching contributions include developing the Business Analytics and Optimization undergraduate minor at Tepper, courses for UBA/MBA/MSBA programs, and被评为“Top 50 Undergraduate Business School Professor” by Poets and Quants. He emphasizes pedagogy integrating advanced algorithms with business applications. Labs/Teams: Active in Relational AI collaborations and affiliated with CMU's machine learning and operations research research groups. His work frequently intersects with industrial partners like Google, Yahoo, and Bosch.

• Multi-Agent Systems
• Human-Robot Interaction
• Game Theory
• +7 more

Katia Sycara is a Research Professor at Carnegie Mellon University's Robotics Institute, part of the School of Computer Science. She also holds the Sixth Century Chair (part-time) in Computing Science at the University of Aberdeen, UK. Her research focuses on multi-agent systems, human-robot interaction, game theory, and adversarial reasoning. She leads the Advanced Agent-Robotics Technology Lab and has developed frameworks like the RETSINA multiagent infrastructure. Education: B.S. in Applied Mathematics (Brown University), M.S. Electrical Engineering (University of Wisconsin), Ph.D. Computer Science (Georgia Tech), Honorary Doctorate (University of the Aegean, 2004). Research interests include semantic web services, agent interoperability, crisis response systems, and negotiation support. She has pioneered work on the DAML-S language for semantic web services and contributed to the OASIS UDDI standard. Notable awards include the 2002 ACM SIGART Agents Research Award and AAAI Fellowship. She has led multimillion-dollar projects funded by DARPA, NASA, AFOSR, and ONR, focusing on applications like autonomous robotics, urban search and rescue, and enterprise integration. Awards: ACM SIGART Autonomous Agents Research Award (2002) AAAI Fellow (2002) IEEE Fellow (2013) Outstanding Alumnus (University of Wisconsin, 2005) Grants and Projects: Command and Control of Multi-Robot Teams (ONR) Games on Graphs (MURI/ONR) Urban Search and Rescue (NASA) Labs/Teams: Director of Advanced Agent-Robotics Technology Lab and contributor to the Semantic Web Science Association.

Stephen SmithView profile

Research Professor

• Artificial Intelligence
• Planning and Scheduling
• Multi-Agent Coordination
• +5 more

Stephen Smith is a Research Professor at the Robotics Institute, Carnegie Mellon University. He directs the Intelligent Coordination and Logistics Laboratory and holds affiliations with Traffic21, Metro21, and the TSET University Transportation Center. His research focuses on AI-driven solutions for planning, scheduling, and coordination in complex systems, including transportation infrastructure, robotics, and manufacturing. Research interests include constraint-based optimization, multi-agent coordination (e.g., robot teams), robust planning under uncertainty, and adaptive traffic control systems like Surtrac. Applications span smart cities, autonomous vehicles, and distributed resource management. He collaborates with municipal partners on projects such as pedestrian safety apps and snowplow route optimization. His work bridges theoretical advances with real-world deployment, emphasizing scalability and practicality. Notable contributions include decentralized traffic control systems, learning-based planning assistants, and frameworks for distributed decision-making in uncertain environments. Current and former advisees include PhD students Hsu-kuang Chiu and Jayanth Krishna Mogali, alongside master’s students like Viraj Parimi. Projects include the PedPal smartphone app and the RADAR cognitive assistant.